Alternatives to the Clustered Bar Chart

We can all agree that 3-d exploding pie charts are pretty rotten.
My vote for worst chart? The clustered bar chart.
I see clustered bar charts everywhere. E-V-E-R-Y-W-H-E-R-E. On pages 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 of reports. In slides 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 of presentations. In every handout, dashboard, and infographic.
Clustered bar charts aren’t inherently bad, but their overuse is killing me. What’s worse than a text-heavy report with zero graphics? A report that only contains the same chart type over and over and over, regardless of whether that chart is really the best tool for the job.
When a colleague was looking to overhaul her clustered bar chart, I jumped at the opportunity.
Let’s explore alternatives to this overused chart. Don’t forget to vote for your favorite alternative at the end of the post.

Before: An Unformatted Clustered Bar Chart

Here’s the chart style I see most often among researchers, analysts, and communications specialists: the unformatted clustered bar chart.
This particular graph is courtesy of Saint Wall Street.

Remake #1: A Formatted Clustered Bar Chart

At the very least, let’s format this chart. Regardless of which software program you’re using, make sure to question every single default setting. Default settings are fine for the privacy of your own computer – when you’re just exploring datasets or drafting several different options. But before charts get shared with coworkers or clients, you’ll need to adjust nearly every setting to ensure that your chart is easy to understand.
Crucial edits include:

I removed the 3d, bringing the graph into the 21st century and making it easier to read.

I flipped the order of the bars. Now, pre-intervention data is listed first and post-intervention data is listed second.

I reduced chart clutter. I removed the border, the grid lines, and the tick marks, none of which served a purpose in our before chart. This streamlined design shocks folks at first, but trust me, sleep on it and you’ll awaken a completely new analyst. Once you get into the habit of reducing chart clutter you’ll never go back to your old ways.

I used the color highlighting strategy. Previously, viewers’ eyes were equally drawn to the orange and the blue. Now, pre-intervention data is a light color and post-intervention data (what we want viewers to focus on) is a deeper, saturated color. I want viewers to know exactly which set of bars is most important.

I swapped the generic title (“Fathers’ Increased Commitment to Family”) for a descriptive title (“Fathers showed an increased commitment to families”). Notice how the title is the largest font in the chart, a technique called hierarchical font sizing.

Remake #2: A Side-by-Side Bar Chart

In theory, this small multiples layout should be easy to read. I purposefully added light gray shading to indicate part-to-whole relationships.
In practice, viewers’ eyes have to zig-zag back and forth between the pre and post data. For instance, they’d have to read the 68%, and then the 75%, and then try to compare the lengths of those bars to each other. Since the bars are beside each other – not on top of each other – comparing the two lengths takes too much precious mental energy. For that reason, the side-by-side bar chart isn’t my favorite option.

Remake #3: A Slope Chart

Slope charts are line charts that only display two points in time. They’re an excellent option for pre-intervention and post-intervention datasets like this. The upward-sloping trend jumps off the screen and into our brains.

Remake #4: A Panel Chart

A small multiples version of the slope chart, this remake allows viewers to examine each of the metrics separately.

Remake #5: A Panel Chart with Contextual Shading

I use this style when I really want viewers to compare the lines to each other. I highlight one line at a time and gray-out the others.

Remake #6: A Dot Plot

Dot plots are another stellar choice for displaying two points in time. I use dot plots for pre/post data, for Grant Year 1/Grant Year 2 data, for Fall/Spring data, and many other timeframes. View additional examples here.Share your perspective in the comments section: Which option do you prefer? Which option(s) do you already use in your own work, and which ones will you try now that you’ve seen them in action?